Sergio Potenciano Menci
SnT-Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, 29 Av. John F. Kennedy, 1855 Luxembourg, LU
Correspondence: Sergio Potenciano Menci (firstname.lastname@example.org)
Energy Informatics 2022, 3 (Suppl 2): S53
Flexibility has risen as a potential solution and complement for system operators’ current and future problems (e.g., congestion, voltage) caused by integrating distributed renewable resources (e.g., wind, solar) and electric vehicles. In parallel, local flexibility markets (LFM) emerge as a possible smart grid solution to bridge between flexibility-seeking customers and flexibility-offering customers in localized areas. Nevertheless, there is no unique, standard, or simple solution to tackle all the problems system operators and other energy actors face. Therefore, many local flexibility market concepts, initiatives (projects), and companies have developed various solutions over the last few years. At the same time, they increased the complexity of the topic. Thus, this research paper aims to describe several local flexibility market concepts, initiatives (projects), and companies in Europe. To do so, we propose a taxonomy derived from LFMs descriptions. We use the taxonomy-building research method proposed by  to develop our taxonomy. Moreover, we use the smart grid architecture model (SGAM) as a structural and foundation guideline. Given the numerous and diverse LFM solutions, we delimit the taxonomy by considering solutions focused on congestion management on medium and low voltage (meta-characteristic).
Keywords: Local Flexibility Markets; SGAM; Taxonomy; Congestion Management
Smart grids in power systems are growing, driven by decentralization, decarbonization, and digitalization trends . Examples of these trends are the integration of distributed energy resource (DER) and electric vehicle (EV) or the massive data created by metering devices (i.e., smart meters). Flexibility is becoming an essential part of current and future energy systems in modern power systems. Notably, they complement traditional solutions to tackle typical power system problems (e.g., congestion, balancing, voltage, grid expansion) caused, for example, by the integration of DERs and EVs. For the most part, DER, particularly renewable sources, located on medium voltage (MV) and low voltage (LV) voltage networks, will provide flexibility. Consequently, the focus shifts to local distribution areas, where the Distribution System Operator (DSO) operates and challenges their planning and operation.
Thus, a rising concept considering the trends and their challenges; and local flexibility as a partial solution over the last years, and even pushed by adaption of the Clean Energy Package (CEP) (i.e., Regulation (EU) 2019/943, Directive (EU) 2019/944), are local flexibility market (LFM). LFM capitalize on asset data extraction and control to provide services at the local area level. The services offered by LFMs can go beyond typical DSO and transmission system operator (TSO) services. An example beyond service is, enabling and managing local energy trading among peers (Peer-to-peer). Consequently, numerous LFM concepts arose within the last five years. For example, Nodes deployed their LFM in several countries (e.g., Norway, Germany, Portugal, Canada, Finland, and the United Kingdom), adapting to their needs . Another example is the Gopacs platform  launched as an initiative by Dutch grid operators. However, the associated literature body is also growing considerably [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. Nevertheless, to date, literature on LFM focuses mainly on clearing algorithms, services development, payments, and trading. Still, one crucial point, the system architecture used to implement these concepts, is not yet fully explored.
Thus, as solutions and literature grow, a description gap can help developers, regulators, and system operators understand the different solutions the market and research offer. Furthermore, as solutions grow, it is inevitable to ask from a research perspective: what are the local flexibility markets archetypes?
The associated benefits of understanding LFM architectures can bring many benefits to developers but especially to both system operators, the TSO and DSO. These benefits are, for instance, to enable the clear identification of LFM scope, what are the main smart functions these LFM bring, clarify which components are necessary for operation, and who owns them and their location or how these components might communicate. There is a mix of what LFM functions currently provide. These solutions involve several stakeholders, not always present in all LFM concepts. Moreover, different LFM solutions implement divergent logic (i.e., coordination of DSO-DSO; DSO-TSO; DSO provision, TSO provision, Peer-2-Peer, etc.). Overall, the topic of LFM is growing fast and encompasses many subtopics challenging its comprehension.
To bridge the description gap and contribute to the research question from a holistic system perspective, we develop a LFM taxonomy. To develop the taxonomy, we use the iterative taxonomy building method proposed by . Moreover, we use the Smart Grid Architecture Model (SGAM) as a structural and foundation guideline since LFM are smart grid solutions.
Arguably, a complete taxonomy of all LFM depends on the level of abstraction. Consequently, we establish the level of abstraction and narrow down the scope by focusing on (1) European developments, (2) congestion management, and (3) medium and low voltage solutions.
Taxonomy building method
We follow the taxonomy building method proposed by . The authors proposed an iterative method for creating a taxonomy, where the meta-characteristic selection sets the taxonomy’s scope. The taxonomy’s end product (i.e., artifact’s structure) contains a set of dimensions (variable), containing each dimension a set of characteristics (possible values of a variable) such that when considering an object (a LFM) has only one characteristic for each dimension. Additionally, we consider the suggestions provided by  to develop taxonomies.
Concerning the iterative part of the method, the authors include two types of iterations. On the one hand, the empirical-to-conceptual iteration describes how the researcher should first identify the set of objects to classify and then identify common characteristics among these objects. To identify the objects, the authors in  recommend a review of the literature using sampling techniques such as random, a systematic, a convenience, or some other type of sample. On the other hand, in the conceptual-to-empirical approach, the research does not examine actual objects but conceptualizes the dimensions. Therefore, this approach is deductive and complicated since it requires experience.
To develop the taxonomy we propose, we aim to use both iteration approaches, empirical-to-conceptual and conceptual-to-empirical. For the empirical-to-conceptual approach, we use scientific, and grey literature available online as previously exposed [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. Furthermore, in the empirical-to-conceptual approach, we might consider a set of interviews with several power system experts; company experts already providing LFMs solutions [17,18,3] and other energy experts (subjective). These interviews might assist us to (1) refine and include aspects to the taxonomy that literature or project’s description might not include and (2) hit the ending conditions when multiple interviewees add no further details. For the conceptual-to-empirical we initially aim to use previous experience from internal European Union (EU) projects (e.g., InteGrid , InterFlex , SynErgie ), grey literature (e.g., public deliverable) and scientific literature (e.g., ) to conceive the taxonomy and identify the first characteristics and domains.
Smart Grid foundations
Energy systems and pointedly smart grids are complex since they encapsulate many aspects. For instance, the smart grid application of LFMs deals with business, regulatory and technical aspects (components, communications, information, and functions). Therefore, to guide the taxonomy in the complex world of smart grids, we would like to use the SGAM  as a foundation for developing the taxonomy. The SGAM provides a standard structure for describing smart grid projects and applications, including almost all aspects. The SGAM could endow the taxonomy with a helpful structure already used in smart grids and thus, increase its usefulness. The SGAM interoperability layers (component, communication, information, function, and business) could assist in identifying better characteristics and dimensions within a specific interoperability layer.
This paper project is in its early stage, and thus, we might not be able to assess the conclusions fully. Nevertheless, the taxonomy might enable energy actors to identify their LFM "archetype" in a clear and structured manner. Additionally, it might bring associated business and development advantages to several actors in the energy and, specifically, in the smart grid industry, such as system operators, regulators and policymakers, and even service providers (developers). System operators might benefit from having a taxonomy to understand the LFM potential impact on their current and future systems. Likewise, regulators and policymakers might benefit from the taxonomy to identify archetypes of LFM that might require different regulations for their possible implementation, for example, distributed ledger technologiess (DLTs) such as Blockchain. Finally, service providers (developers) might also benefit from such a taxonomy as they could use it for clear business development and differentiation between solutions. Additionally, the taxonomy could be material for follow-up research, such as evaluating the taxonomy’s usefulness by classifying a couple of LFMs currently available in literature or commercially available.
The author gratefully acknowledges PayPal and the Luxembourg National Research Fund FNR (P17/IS/13342933/PayPal-FNR/Chair in DFS/ Gilbert Fridgen). Additionally the author would like to thank his primary advisor, Prof. Dr. Fridgen and his shepherd Prof. Dr. Vartiainen, and the reviewers for their helpful comments and suggestions.
This work is funded by by the Kopernikus-Project “SynErgie” by the Federal Ministry of Education and Research of Germany (BMBF) and the project supervision by the project management organization Projektträger Jülich (PtJ).
Availability of data and materials
SPM contributed to the design of the work. SPM drafted the first version of the paper.
The authors declare that they have no competing interests.
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